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Original Contribution |

Receipt of Disease-Modifying Antirheumatic Drugs Among Patients With Rheumatoid Arthritis in Medicare Managed Care Plans FREE

Gabriela Schmajuk, MD, MS; Amal N. Trivedi, MD, MPH; Daniel H. Solomon, MD, MPH; Edward Yelin, PhD; Laura Trupin, MPH; Eliza F. Chakravarty, MD, MS; Jinoos Yazdany, MD, MPH
[+] Author Affiliations

Author Affiliations: Department of Medicine, Division of Rheumatology, Stanford University, Stanford, California (Drs Schmajuk and Chakravarty); Department of Community Health and Medicine, Brown University, Providence, Rhode Island (Dr Trivedi); Department of Medicine, Division of Rheumatology, Brigham and Women's Hospital, Harvard University, Boston, Massachusetts (Dr Solomon); and Department of Medicine, Division of Rheumatology,University of California, San Francisco, San Francisco (Dr Yelin, Ms Trupin, and Dr Yazdany).


JAMA. 2011;305(5):480-486. doi:10.1001/jama.2011.67.
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Published online

Context In 2005, the Healthcare Effectiveness Data and Information Set (HEDIS) introduced a quality measure to assess the receipt of disease-modifying antirheumatic drugs (DMARDs) among patients with rheumatoid arthritis (RA).

Objective To identify sociodemographic, community, and health plan factors associated with DMARD receipt among Medicare managed care enrollees.

Design, Setting, and Participants We analyzed individual-level HEDIS data for 93 143 patients who were at least 65 years old with at least 2 diagnoses of RA within a measurement year (during 2005-2008). Logistic regression models with generalized estimating equations were used to determine factors associated with DMARD receipt and logistic regression was used to adjust health plan performance for case mix.

Main Outcome Measures Receipt or nonreceipt of DMARD.

Results The mean age of patients was 74 years; 75% were women and 82% were white. Overall performance on the HEDIS measure for RA was 59% in 2005, increasing to 67% in 2008 (P for trend <.001). The largest difference in performance was based on age: patients aged 85 years and older had a 30 percentage point lower rate of DMARD receipt (95% confidence interval [CI], −29 to −32 points; P < .001), compared with patients 65 to 69 years of age, even after adjusting for other factors. Lower percentage point rates were also found for patients who were men (−3 points; 95% CI, −5 to −2 points; P < .001), of black race (−4 points; 95% CI, −6 to −2 points; P < .001), with low personal income (−6 points; 95% CI, −8 to −5 points; P < .001), with the lowest zip code–based socioeconomic status (−4 points; 95% CI, −6 to 2 points; P < .001), or enrolled in for-profit health plans (−4 points; 95% CI, −7 to 0 points; P < .001); and in the Middle Atlantic region (−7 points; 95% CI, −13 to −2 points; P < .001) and South Atlantic regions (−11 points; 95% CI, −20 to −3 points; P < .001) as compared with the Pacific region. Performance varied widely by health plan, ranging from 16% to 87%.

Conclusions Among Medicare managed care enrollees carrying a diagnosis of RA between 2005 and 2008, 63% received a DMARD. Receipt of DMARDs varied based on demographic factors, socioeconomic status, geographic location, and health plan.

Figures in this Article

Despite evidence-based guidelines recommending early and aggressive treatment of active rheumatoid arthritis (RA),1,2 recent population-based studies of disease-modifying antirheumatic drug (DMARD) use in patients with RA report consistently low rates of DMARD receipt (30%-52%).37 One limitation of the existing literature is that US studies have only examined groups with low socioeconomic status covered by state-funded insurance programs or within a single health plan and geographic area; therefore, these data may not be broadly generalizable.46

In 2005, the National Committee for Quality Assurance revised the Healthcare Effectiveness Data and Information Set (HEDIS), a set of performance indicators used by health plans to report on their quality of care, to include a measure assessing whether patients with RA received a prescription for a DMARD. Medicare managed care plans (alternatives to traditional Medicare fee-for-service programs that provide hospital, outpatient, and pharmacy coverage to more than 8 million Medicare beneficiaries) are required to report their performance on all HEDIS measures. Therefore, HEDIS data from Medicare managed care plans provide a nationally representative sample of the managed care population older than 65 years and avoid the selection bias associated with studies of patients in specialty care or in health plans with voluntary reporting.

In this study, we examined variations in DMARD receipt in a large cohort of managed care patients diagnosed with RA. To our knowledge, this is the first comprehensive study to assess sociodemographic, community, and health plan factors associated with DMARD receipt.

Study Population and Data Sources

Individual-level HEDIS data were used for all Medicare managed care plans from the Centers for Medicare & Medicaid Services (CMS) for 4 reporting years (2006-2009), covering clinical care delivered (2005-2008). Each observation included a participant's health identification code, health plan name, and variables indicating eligibility for and receipt of care consistent with the HEDIS RA measure. The study protocol was approved by the institutional review boards of Stanford University and the University of California, San Francisco and by the CMS privacy board; an exemption to informed consent was granted because the data set used was deidentified.

Information about data collection and CMS-sponsored audits has been published previously.8,9 Using each participant's identification code, we matched beneficiaries eligible for the HEDIS RA measure with the Medicare denominator file for the corresponding year. This file contains demographic information on race, age, sex, zip code, and county of residence. The file also contains information on whether beneficiaries had received state buy-in assistance for their Medicare Part B premiums or co-payments, a marker of low personal income.10 We achieved a match rate of 98%. Zip code–based socioeconomic status was calculated using variables from the 2000 US Census and the Agency for Healthcare Research and Quality's socioeconomic status index score.11 Health professional shortage area counties (those with a complete or partial shortage of physicians) were defined using the Area Resource File.12 We obtained health plan characteristics (model type, plan age, enrollment size, and tax status) from the Interstudy Competitive Edge data set or by contacting the health plans directly.13

Inclusion criteria were eligibility for the HEDIS RA measure per the specification, participant age of at least 65 years, residence in the 50 US states, and survival through the measurement year. There were 180 153 observations that met these criteria. We excluded 94 health plans (with 12 862 observations) that appeared to have incomplete reporting, as evidenced by missing data for more than 10% of eligible enrollees or less than 10% performance on the HEDIS RA measure. Because the data set covers multiple years, individuals could have as many as 4 observations; we selected 1 observation at random from each individual to avoid multiple levels of clustering in the statistical analysis. The final study sample included 93 143 observations from 299 health plans.

Measure Specification

The National Committee for Quality Assurance's HEDIS RA measure aimed to assess “whether patients diagnosed with RA have had at least one ambulatory prescription dispensed for a DMARD [during the measurement year].”14 Patients in the denominator for the measure: (1) were continuously enrolled in a Medicare managed care plan during the measurement year (no more than one 45-day gap in enrollment allowed); (2) had both medical and pharmacy benefits; and (3) had at least 2 face-to-face physician encounters with different dates of service in an ambulatory or nonacute patient setting during the measurement year with any diagnosis of RA (International Classification of Diseases, Ninth Revision [ICD-9 ] codes 714.0, 714.1, 714.2, or 714.81). Patients who were pregnant or carried a diagnosis of human immunodeficiency virus during the measurement year were excluded from the measure. Accepted drugs included traditional and biologic DMARDs: abatacept, adalimumab, anakinra, azathioprine, cyclophosphamide, cyclosporine, etanercept, gold, hydroxychloroquine, infliximab, leflunomide, methotrexate, minocycline, penicillamine, rituximab, staphylococcal protein A, and sulfasalazine. The numerator for the measure was a dichotomous measure of DMARD receipt (yes/no); the names of the specific DMARDs received were not recorded.

Study Variables

The dependent variable was DMARD receipt among eligible enrollees. Independent variables included age, race (white, black, other), sex, low personal income (as proxied by the state buy-in variable), zip code–based socioeconomic status index score (divided into quintiles), residence in a physician shortage county, geographic region, and health plan variables (model type, plan age, enrollment size, and tax status). Information on race in the Medicare denominator file was derived from Social Security Administration data obtained at the time of an individual's application for a new or replacement Social Security card and has been shown to be reliable for individuals designated as white or black.15,16

Statistical Analysis

We assessed demographic, socioeconomic, and health plan characteristics of participants in the final study sample and used χ2 or t tests to compare participants receiving DMARDs with those who were not. We determined the overall and yearly performance on the HEDIS RA measure. For each subgroup of participants (defined by demographic, socioeconomic, and health plan characteristics), we calculated performance on the HEDIS RA measure and the absolute difference in performance compared with the relevant referent group.

To determine adjusted performance differences among subgroups, we fitted linear models with generalized estimating equations to predict DMARD receipt, adjusted for covariates and accounting for the clustering of individuals within health plans.17 Variables in the multivariate models were determined a priori based on prior studies of DMARD use and HEDIS measure fulfillment.8 All covariates were tested to ensure noncollinearity. We fitted 3 versions of these models: model 1 adjusted for age, race, sex, income, and year; model 2 included all variables in model 1 in addition to zip code–based socioeconomic status, geographic region, and residence in a physician shortage county; and model 3 included all variables in model 2 in addition to health plan variables (model type, plan age, enrollment size, and tax status). Results of the models were reported as percentage point differences. We performed additional sensitivity analyses using the first year of data contributed by a given individual, the last year of data contributed by an individual, and using the “best case” year of data contributed by an individual (ie, if the participant received a DMARD in some years but not others, categorizing the participant as having received a DMARD).

Last, we assessed the variability in the performance of different health plans on the HEDIS RA measure. Health plans with fewer than 20 beneficiaries in the final study sample were excluded, leaving 245 plans. Health plan performance on the measure was calculated by aggregating the individual-level data. We assessed performance on the HEDIS RA measure with logistic regression models to adjust health plan performance for case mix (including variables for age, race, sex, income as proxied by state buy-in, zip code–based socioeconomic status, residence in a physician shortage county, and geographic region). This process has been described at length elsewhere.18 Briefly, we applied regression coefficients from multivariate logistic regressions to calculate the predicted probability of DMARD receipt in each health plan for every individual in the combined sample. By averaging the predicted probabilities by health plan, we calculated a directly standardized adjusted performance rate, defined as the predicted performance for each plan if every plan had the same distribution of member characteristics.

Statistical tests were 2-sided with P < .05 considered statistically significant. We were able to detect a minimum difference of 2.2 percentage points in the individual subgroup analyses with 80% power. All analyses were performed using SAS statistical software (version 9.2; Cary, North Carolina).

The characteristics of the individual participants and health plans in the study sample are listed in Table 1 and Table 2. Overall performance on the HEDIS RA measure in the study sample was 63%. In 2005, 59% of the sample received a DMARD; in 2006, 58%; in 2007, 62%; and in 2008, 67% (P for trend <.001).

Table Graphic Jump LocationTable 1. Sociodemographic Characteristics of Patients Eligible for the HEDIS Rheumatoid Arthritis Measure and Characteristics of Health Plansa
Table Graphic Jump LocationTable 2. Characteristics of Health Plans and Patients Eligible for the HEDIS Rheumatoid Arthritis Measurea

The largest difference in performance on the HEDIS RA measure was based on age: participants aged 85 years and older had a 30 percentage point lower rate of DMARD receipt (95% CI 29-32 points) compared with patients 65 to 69 years old, even after adjusting for other factors (Table 3). Other participant categories less likely to receive a DMARD were men, individuals identified by race as black or other, individuals with low personal income, participants in lower socioeconomic status zip codes, and individuals in the Middle and South Atlantic regions. Patients living in a health professional shortage area had slightly lower performance (−3 percentage points; 95% CI, −1 to −5 points). In addition, patients enrolled in a for-profit health plan had a 4 percentage point lower rate of DMARD receipt (95% CI, 0 to −7 points) compared with patients enrolled in a not-for-profit health plan.

Table Graphic Jump LocationTable 3. Observed and Adjusted Performance Rates on the HEDIS Rheumatoid Arthritis Measure

A model similar to model 3 in which the separate measures of personal and neighborhood socioeconomic status were replaced by 1 categorical variable representing the 4 possible combinations for personal income (low/not low) and lowest quintile of zip code–based socioeconomic status (yes/no) revealed a stepped relationship. Individuals with both personal and neighborhood poverty had the lowest performance on the HEDIS RA measure (−7.1 percentage points; 95% CI, −9.8 to −4.4 points) compared with those without low personal income and living in any of the top 4 quintiles of socioeconomic status zip codes, individuals with only 1 type of poverty—a slightly better living standard (low personal income only, −6.9 percentage points; 95% CI, −8.9 to −5.0 points), and lowest socioeconomic status zip codes only (−2.4 percentage points; 95% CI, −4.1 to −0.8 points).

Sensitivity analyses in which the first or the most recent year of data contributed by an individual did not change the results of the multivariate models. Similarly, using the best case year of data showed that 66% of patients received a DMARD overall. Again, the results of the multivariate models remained unchanged.

Performance varied widely by health plan (Figure) with rates ranging from 16% to 87%, even after adjusting for case mix. The range of adjustment due to case mix was −16% to +21% (mean, −1%; interquartile range, −4% to 1%). The case mix adjustments were smaller than the standard error of the rates for the majority of plans; in other words, the variation introduced into the performance rates by case mix was smaller than the sampling variability, but not negligible. A sensitivity analysis using the best case year of data only changed adjusted health plan performance slightly (range, 15%-89%).

Place holder to copy figure label and caption
Figure. Observed and Case Mix–Adjusted Rate of Performance by Health Plans on the Healthcare Effectiveness Data and Information Set (HEDIS) Rheumatoid Arthritis Measure (n = 245)
Graphic Jump Location

Plans with fewer than 20 observations were excluded.

In this nationally representative sample of patients in Medicare managed care with a diagnosis of RA, we found wide variations in DMARD receipt based on sociodemographics, geographic location, and health plan. Prior research has found that disparities in outcomes for patients with RA exist on the basis of race and socioeconomic status.1922 Although RA was once an inevitably deforming and disabling condition, the development of new DMARDs and support for their early use has dramatically improved clinical outcomes for many patients.1,2325 This study suggests that 1 mechanism for the sociodemographic disparities in RA outcomes in the United States may relate to differences in DMARD receipt.

Although we found that more than one-third of patients in this cohort were not receiving a DMARD, the optimal rate of DMARD receipt for this cohort is difficult to estimate. Some patients in the HEDIS denominator for this measure may have declined treatment, had quiescent disease, or had contraindications to all 17 eligible drugs. In a recent study using the German Biologic Register, Ziegler et al26 report that as many as 19% of patients did not receive a DMARD within a calendar year and that as many as 11% had quiescent disease or relative contraindications to available drugs. In our study, the proportion of patients in these categories may be even higher because of an older mean age and possibly a higher rate of RA misdiagnosis (because RA diagnoses were drawn from administrative sources).

We found that DMARD receipt varied significantly with sex and age; the oldest patients had the lowest rate of DMARD receipt and men had slightly lower DMARD receipt compared with women. Although there are conflicting data around disparities based on sex, recent studies have shown that older individuals are less likely to receive DMARDs.3,4,7,2729 Age differences may be due to age bias,27 increased prevalence of comorbidities that may represent contraindications to DMARD use,4 patient preferences against DMARD receipt, or a milder or different clinical course among older patients. Future studies using a large clinical data set could further elucidate these issues.

In addition, we found that individuals with low personal income and those living in low socioeconomic status neighborhoods had reduced DMARD receipt. Lacaille et al28 report similar findings in a population-based study from Canada. Low neighborhood socioeconomic status exerted an independent negative effect on DMARD receipt beyond that of personal low income; this finding has been described previously in studies of physical and mental health outcomes and access to care in patients with other chronic conditions.30,31

This study found significant variation in performance on the HEDIS RA measure based on geography, with patients in the Middle and South Atlantic regions using DMARDs at rates 10 percentage points lower than patients in other areas of the United States, even after adjusting for characteristics of individual patients. Such geographic disparities have been noted in other analyses of Medicare quality and spending.3234 This study also found a small but significant increase in DMARD receipt in enrollees of not-for-profit health plans compared with enrollees of for-profit health plans, which has also been reported for other HEDIS measures.35

The dramatic variations in performance on the HEDIS RA measure among different health plans are concerning. This study found a 70 percentage point spread between the best and worst performing plans. Possible explanations for these differences include variations in the availability or accessibility of specialty care within a health plan or differences in the ability of the health plan to appropriately identify patients in the numerator and denominator for the measure. At least 1 other study has found that health plans can influence quality of care independent of the selection of physician organizations with which they contract.36

The primary strength of this study is that it provides the first examination of DMARD receipt in a nationally representative sample of patients in managed care plans. Because we enhanced data obtained from HEDIS with multiple other data sources, we were able to investigate the influence of individual, community, and health care system factors on DMARD receipt.

However, the study has several limitations. RA diagnoses were obtained from administrative sources. Gabriel's population-based study measuring the accuracy of administrative diagnoses for RA showed only moderate (57%) positive predictive value of a single diagnostic code for RA compared with a criterion standard that included medical record review.37 Identification of RA patients in this study were based on 2 RA diagnosis codes from 2 face-to-face physician encounters with different dates of service; still, some patients in this study may have been misclassified as having RA when in fact they carried an alternate diagnosis or had quiescent disease that might not require DMARDs.

In addition, we lacked detailed clinical information on factors such as disease activity, comorbidities, contraindications to DMARD use, or patient preferences. However, the number of patients with contraindications or intolerance to all 17 available DMARDs (including hydroxychloroquine and sulfasalazine) was likely to be small, as demonstrated by very high rates of DMARD receipt reported for patients seen in specialty clinics.26 We did not have information on the treating physician's specialty for each individual patient or the number of rheumatology specialists serving a particular health plan. Because physician specialty has been identified as an important predictor of quality care in other studies,4,5,28 this represents a possible explanation for the disparities we observed in this study's population. However, we did include a variable (health professional shortage areas) that assessed the density of physicians, including specialists, in a patient's county and found that patients residing in a physician shortage county were significantly less likely to receive a DMARD.

Finally, we were not able to assess the allowances on prescription drug benefits for each health plan, which may be an important factor associated with DMARD receipt. Although annual out-of-pocket costs among patients in Medicare managed care plans for biologic DMARDs can exceed $4000,38 drugs such as hydroxychloroquine or methotrexate cost less than $1000 per year. Still, patients with state buy in or living in lower socioeconomic status neighborhoods might be less likely to receive a DMARD because they are unable to afford copayments or other forms of cost sharing for a DMARD.39 If patients with lowest socioeconomic status were clustered within a few health plans, cost burden may also be a mechanism for the variation found in health plan performance.

In summary, we found significant differences in DMARD receipt based on individual, community, and health plan characteristics. Given the enormous individual and societal costs associated with RA, and increasing substantial evidence that DMARDs can reduce these costs, variations in DMARD receipt based on demographics, socioeconomic status, and geography are unacceptable. Because optimizing DMARD use is the primary mechanism for decreasing the significant public health impact of RA in the United States, targeting educational and quality improvement interventions to patients who are underusing DMARDs and their clinicians will be important to eliminate these disparities. Additional studies of population-wide cohorts that include clinical data and disease activity measures are needed to validate our findings.

Corresponding Author: Gabriela Schmajuk, MD, MS, Stanford University, Department of Medicine, Division of Rheumatology, 1000 Welch Rd, Ste 203, Stanford, CA 94304 (schmajuk@gmail.com).

Author Contributions: Dr Schmajuk had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Schmajuk, Trivedi, Yelin, Yazdany.

Acquisition of data: Schmajuk, Yazdany.

Analysis and interpretation of data: Schmajuk, Trivedi, Solomon, Chakravarty, Trupin, Yazdany.

Drafting of the manuscript: Schmajuk, Yelin, Yazdany.

Critical revision of the manuscript for important intellectual content: Schmajuk, Trivedi, Solomon, Yelin, Chakravarty, Trupin, Yazdany.

Statistical analysis: Schmajuk, Yelin, Trupin, Yazdany.

Obtained funding: Schmajuk, Yelin, Yazdany.

Administrative, technical, or material support: Trivedi, Yazdany.

Study supervision: Solomon, Chakravarty, Yazdany.

Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Dr Trivedi reports receipt of compensation from Merck (for editing a chapter of the Merck Manual) and from the Pzifer (for receiving a Health Policy Scholars Award from the Pzifer Foundation). Other authors reported no potential conflicts.

Funding/Support: Funding was provided by the American College of Rheumatology/Research and Education Foundation Physician-Scientist Development Award; National Center for Research Resources (5-M01-RR-00079); Rosalind Russell Medical Research Center for Arthritis; National Institutes of Health (R01-AR-44804); State of California Lupus Fund; Arthritis Foundation; Agency for Healthcare Research and Quality; and National Institute of Arthritis and Musculoskeletal and Skin Diseases (grant numbers: 1-R01-HS-013893, P60-AR-053308, K24-AR-AR055989, and R01-AR-056215).

Role of the Sponsor: The funding organizations had no role in the design and conduct of the study, in the collection, analysis, and interpretation of the data, or in the preparation, review, or approval of the manuscript.

Additional Contributions: We would like to thank Alan Zaslavsky, PhD, Department of Health Care Policy, Harvard Medical School, for sharing his case mix adjustment macro. Dr Zaslavsky was not compensated for this contribution.

American College of Rheumatology Subcommittee on Clinical Guidelines.  Guidelines for the management of RA: 2002 update.  Arthritis Rheum. 2002;46:328
PubMed   |  Link to Article
Saag KG, Teng GG, Patkar NM,  et al; American College of Rheumatology.  American College of Rheumatology 2008 recommendations for the use of nonbiologic and biologic disease-modifying antirheumatic drugs in rheumatoid arthritis.  Arthritis Rheum. 2008;59(6):762-784
PubMed   |  Link to Article
Edwards CJ, Arden NK, Fisher D,  et al.  The changing use of disease-modifying anti-rheumatic drugs in individuals with rheumatoid arthritis from the United Kingdom General Practice Research Database.  Rheumatology (Oxford). 2005;44(11):1394-1398
PubMed   |  Link to Article
Schmajuk G, Schneeweiss S, Katz JN,  et al.  Treatment of older adult patients diagnosed with rheumatoid arthritis.  Arthritis Rheum. 2007;57(6):928-934
PubMed   |  Link to Article
Khanna R, Smith MJ. Utilization and costs of medical services and prescription medications for rheumatoid arthritis among recipients covered by a state Medicaid program.  Clin Ther. 2007;29(11):2456-2467
PubMed   |  Link to Article
Grijalva CG, Chung CP, Stein CM, Mitchel EF Jr, Griffin MR. Changing patterns of medication use in patients with rheumatoid arthritis in a Medicaid population.  Rheumatology (Oxford). 2008;47(7):1061-1064
PubMed   |  Link to Article
Widdifield J, Bernatsky S, Paterson JM, Thorne JC, Cividino A, Pope J, Gunraj N, Bombardier C. Quality care in seniors with new-onset rheumatoid arthritis.  Arthritis Care Res (Hoboken). 2011;63(1):53-57
PubMed   |  Link to Article
Trivedi AN, Zaslavsky AM, Schneider EC, Ayanian JZ. Trends in the quality of care and racial disparities in Medicare managed care.  N Engl J Med. 2005;353(7):692-700
PubMed   |  Link to Article
Health Care Financing Administration.  Medicare HEDIS data audits: Medicare HEDIS 3.0/1998 data audit report. http://permanent.access.gpo.gov/websites/www.hcfa.gov/quality/3i2.htm. Accessed August 2, 2010
Koroukian SM, Dahman B, Copeland G, Bradley CJ. The utility of the state buy-in variable in the Medicare denominator file to identify dually eligible Medicare-Medicaid beneficiaries.  Health Serv Res. 2010;45(1):265-282
PubMed   |  Link to Article
Agency for Healthcare Research and Quality.  Creation of new race-ethnicity codes and socioeconomic status (SES) indicators for Medicare beneficiaries: final report, AHRQ publication no. 08-0029-ef, January 2008. http://www.ahrq.gov/qual/medicareindicators/. Accessed February 11, 2010
Best AE. Secondary data bases and their use in outcomes research.  J Med Syst. 1999;23(3):175-181
PubMed   |  Link to Article
 The InterStudy Competitive Edge 12.1 [database]. St Paul, MN: InterStudy Publications; 2001
Agency for Healthcare Research and Quality.  Rheumatoid arthritis: percentage of members who were diagnosed with rheumatoid arthritis and who were dispensed at least one ambulatory prescription for a disease modifying anti-rheumatic drug (DMARD). http://qualitymeasures.ahrq.gov/content.aspx?id=14957#Section304. Accessed January 11, 2011
Sequist TD, Schneider EC. Addressing racial and ethnic disparities in health care.  Health Serv Res. 2006;41(4 pt 1):1451-1468
PubMed
Arday SL, Arday DR, Monroe S, Zhang J.  HCFA's racial and ethnic data.  Health Care Financ Rev. 2000;21(4):107-116
PubMed
Liang KY, Zeger SL. Longitudinal data analysis using generalized linear models.  Biometrika. 1986;73(1):13-22Link to Article
Link to Article
Zaslavsky AM, Epstein AM. How patients' sociodemographic characteristics affect comparisons of competing health plans in California on HEDIS quality measures.  Int J Qual Health Care. 2005;17(1):67-74
PubMed   |  Link to Article
Groessl EJ, Ganiats TG, Sarkin AJ. Sociodemographic differences in quality of life in RA.  Pharmacoeconomics. 2006;24(2):109-121
PubMed   |  Link to Article
Bruce B, Fries JF, Murtagh KN. Health status disparities in ethnic minority patients with rheumatoid arthritis.  J Rheumatol. 2007;34(7):1475-1479
PubMed
Iren UT, Walker MS, Hochman E, Brasington R. A pilot study to determine whether disability and disease activity are different in African American and Caucasian patients with rheumatoid arthritis in St Louis, Missouri, USA.  J Rheumatol. 2005;32(4):602-608
PubMed
Brunner HI, Taylor J, Britto MT,  et al.  Differences in disease outcomes between medicaid and privately insured children.  Arthritis Rheum. 2006;55(3):378-384
PubMed   |  Link to Article
Pincus T, O'Dell JR, Kremer JM. Combination therapy with multiple disease-modifying antirheumatic drugs in rheumatoid arthritis.  Ann Intern Med. 1999;131(10):768-774
PubMed   |  Link to Article
Pisetsky DS, St Clair EW. Progress in the treatment of rheumatoid arthritis.  JAMA. 2001;286:2787-2790
PubMed   |  Link to Article
Strand V, Singh JA. Improved health-related quality of life with effective disease-modifying antirheumatic drugs.  Am J Manag Care. 2008;14(4):234-254
PubMed
Ziegler S, Huscher D, Karberg K, Krause A, Wassenberg S, Zink A. Trends in treatment and outcomes of rheumatoid arthritis in Germany 1997-2007.  Ann Rheum Dis. 2010;69(10):1803-1808
PubMed   |  Link to Article
Tutuncu A, Reed G, Kremer J, Kavanaugh A. Do patients with older onset rheumatoid arthritis receive less aggressive treatment?  Ann Rheum Dis. 2006;65(9):1226-1229
PubMed   |  Link to Article
Lacaille D, Anis AH, Guh DP, Esdaile JM. Gaps in care for rheumatoid arthritis.  Arthritis Rheum. 2005;53(2):241-248
PubMed   |  Link to Article
Sokka T, Toloza S, Cutolo M,  et al; QUEST-RA Group.  Women, men, and rheumatoid arthritis.  Arthritis Res Ther. 2009;11(1):R7
PubMed
Brown AF, Ang A, Pebley AR. The relationship between neighborhood characteristics and self-rated health for adults with chronic conditions.  Am J Public Health. 2007;97(5):926-932
PubMed   |  Link to Article
Trupin L, Tonner MC, Yazdany J,  et al.  The role of neighborhood and individual socioeconomic status in outcomes of systemic lupus erythematosus.  J Rheumatol. 2008;35(9):1782-1788
PubMed
Zuckerman S, Waidmann T, Berenson R, Hadley J. Clarifying sources of geographic differences in Medicare spending.  N Engl J Med. 2010;363(1):54-62
PubMed   |  Link to Article
Song Y, Skinner J, Bynum J, Sutherland J, Wennberg JE, Fisher ES. Regional variations in diagnostic practices.  N Engl J Med. 2010;363(1):45-53
PubMed   |  Link to Article
Keenan PS, Cleary PD, O’Malley AJ, Landon BE, Zaborski L, Zaslavsky AM. Geographic area variations in the Medicare health plan era.  Med Care. 2010;48(3):260-266
PubMed   |  Link to Article
Schneider EC, Zaslavsky AM, Epstein AM. Quality of care in for-profit and not-for-profit health plans enrolling Medicare beneficiaries.  Am J Med. 2005;118(12):1392-1400
PubMed   |  Link to Article
Baker LC, Hopkins DS. The contribution of health plans and provider organizations to variations in measured plan quality.  Int J Qual Health Care. 2010;22(3):210-218
PubMed   |  Link to Article
Gabriel SE. The sensitivity and specificity of computerized databases for the diagnosis of rheumatoid arthritis.  Arthritis Rheum. 1994;37(6):821-823
PubMed   |  Link to Article
Polinski JM, Mohr PE, Johnson L. Impact of Medicare Part D on access to and cost sharing for specialty biologic medications for beneficiaries with rheumatoid arthritis.  Arthritis Rheum. 2009;61(6):745-754
PubMed   |  Link to Article
Gleason PP, Starner CI, Gunderson BW, Schafer JA, Sarran HS. Association of prescription abandonment with cost share for high-cost specialty pharmacy medications.  J Manag Care Pharm. 2009;15(8):648-658
PubMed

Figures

Place holder to copy figure label and caption
Figure. Observed and Case Mix–Adjusted Rate of Performance by Health Plans on the Healthcare Effectiveness Data and Information Set (HEDIS) Rheumatoid Arthritis Measure (n = 245)
Graphic Jump Location

Plans with fewer than 20 observations were excluded.

Tables

Table Graphic Jump LocationTable 1. Sociodemographic Characteristics of Patients Eligible for the HEDIS Rheumatoid Arthritis Measure and Characteristics of Health Plansa
Table Graphic Jump LocationTable 2. Characteristics of Health Plans and Patients Eligible for the HEDIS Rheumatoid Arthritis Measurea
Table Graphic Jump LocationTable 3. Observed and Adjusted Performance Rates on the HEDIS Rheumatoid Arthritis Measure

References

American College of Rheumatology Subcommittee on Clinical Guidelines.  Guidelines for the management of RA: 2002 update.  Arthritis Rheum. 2002;46:328
PubMed   |  Link to Article
Saag KG, Teng GG, Patkar NM,  et al; American College of Rheumatology.  American College of Rheumatology 2008 recommendations for the use of nonbiologic and biologic disease-modifying antirheumatic drugs in rheumatoid arthritis.  Arthritis Rheum. 2008;59(6):762-784
PubMed   |  Link to Article
Edwards CJ, Arden NK, Fisher D,  et al.  The changing use of disease-modifying anti-rheumatic drugs in individuals with rheumatoid arthritis from the United Kingdom General Practice Research Database.  Rheumatology (Oxford). 2005;44(11):1394-1398
PubMed   |  Link to Article
Schmajuk G, Schneeweiss S, Katz JN,  et al.  Treatment of older adult patients diagnosed with rheumatoid arthritis.  Arthritis Rheum. 2007;57(6):928-934
PubMed   |  Link to Article
Khanna R, Smith MJ. Utilization and costs of medical services and prescription medications for rheumatoid arthritis among recipients covered by a state Medicaid program.  Clin Ther. 2007;29(11):2456-2467
PubMed   |  Link to Article
Grijalva CG, Chung CP, Stein CM, Mitchel EF Jr, Griffin MR. Changing patterns of medication use in patients with rheumatoid arthritis in a Medicaid population.  Rheumatology (Oxford). 2008;47(7):1061-1064
PubMed   |  Link to Article
Widdifield J, Bernatsky S, Paterson JM, Thorne JC, Cividino A, Pope J, Gunraj N, Bombardier C. Quality care in seniors with new-onset rheumatoid arthritis.  Arthritis Care Res (Hoboken). 2011;63(1):53-57
PubMed   |  Link to Article
Trivedi AN, Zaslavsky AM, Schneider EC, Ayanian JZ. Trends in the quality of care and racial disparities in Medicare managed care.  N Engl J Med. 2005;353(7):692-700
PubMed   |  Link to Article
Health Care Financing Administration.  Medicare HEDIS data audits: Medicare HEDIS 3.0/1998 data audit report. http://permanent.access.gpo.gov/websites/www.hcfa.gov/quality/3i2.htm. Accessed August 2, 2010
Koroukian SM, Dahman B, Copeland G, Bradley CJ. The utility of the state buy-in variable in the Medicare denominator file to identify dually eligible Medicare-Medicaid beneficiaries.  Health Serv Res. 2010;45(1):265-282
PubMed   |  Link to Article
Agency for Healthcare Research and Quality.  Creation of new race-ethnicity codes and socioeconomic status (SES) indicators for Medicare beneficiaries: final report, AHRQ publication no. 08-0029-ef, January 2008. http://www.ahrq.gov/qual/medicareindicators/. Accessed February 11, 2010
Best AE. Secondary data bases and their use in outcomes research.  J Med Syst. 1999;23(3):175-181
PubMed   |  Link to Article
 The InterStudy Competitive Edge 12.1 [database]. St Paul, MN: InterStudy Publications; 2001
Agency for Healthcare Research and Quality.  Rheumatoid arthritis: percentage of members who were diagnosed with rheumatoid arthritis and who were dispensed at least one ambulatory prescription for a disease modifying anti-rheumatic drug (DMARD). http://qualitymeasures.ahrq.gov/content.aspx?id=14957#Section304. Accessed January 11, 2011
Sequist TD, Schneider EC. Addressing racial and ethnic disparities in health care.  Health Serv Res. 2006;41(4 pt 1):1451-1468
PubMed
Arday SL, Arday DR, Monroe S, Zhang J.  HCFA's racial and ethnic data.  Health Care Financ Rev. 2000;21(4):107-116
PubMed
Liang KY, Zeger SL. Longitudinal data analysis using generalized linear models.  Biometrika. 1986;73(1):13-22Link to Article
Link to Article
Zaslavsky AM, Epstein AM. How patients' sociodemographic characteristics affect comparisons of competing health plans in California on HEDIS quality measures.  Int J Qual Health Care. 2005;17(1):67-74
PubMed   |  Link to Article
Groessl EJ, Ganiats TG, Sarkin AJ. Sociodemographic differences in quality of life in RA.  Pharmacoeconomics. 2006;24(2):109-121
PubMed   |  Link to Article
Bruce B, Fries JF, Murtagh KN. Health status disparities in ethnic minority patients with rheumatoid arthritis.  J Rheumatol. 2007;34(7):1475-1479
PubMed
Iren UT, Walker MS, Hochman E, Brasington R. A pilot study to determine whether disability and disease activity are different in African American and Caucasian patients with rheumatoid arthritis in St Louis, Missouri, USA.  J Rheumatol. 2005;32(4):602-608
PubMed
Brunner HI, Taylor J, Britto MT,  et al.  Differences in disease outcomes between medicaid and privately insured children.  Arthritis Rheum. 2006;55(3):378-384
PubMed   |  Link to Article
Pincus T, O'Dell JR, Kremer JM. Combination therapy with multiple disease-modifying antirheumatic drugs in rheumatoid arthritis.  Ann Intern Med. 1999;131(10):768-774
PubMed   |  Link to Article
Pisetsky DS, St Clair EW. Progress in the treatment of rheumatoid arthritis.  JAMA. 2001;286:2787-2790
PubMed   |  Link to Article
Strand V, Singh JA. Improved health-related quality of life with effective disease-modifying antirheumatic drugs.  Am J Manag Care. 2008;14(4):234-254
PubMed
Ziegler S, Huscher D, Karberg K, Krause A, Wassenberg S, Zink A. Trends in treatment and outcomes of rheumatoid arthritis in Germany 1997-2007.  Ann Rheum Dis. 2010;69(10):1803-1808
PubMed   |  Link to Article
Tutuncu A, Reed G, Kremer J, Kavanaugh A. Do patients with older onset rheumatoid arthritis receive less aggressive treatment?  Ann Rheum Dis. 2006;65(9):1226-1229
PubMed   |  Link to Article
Lacaille D, Anis AH, Guh DP, Esdaile JM. Gaps in care for rheumatoid arthritis.  Arthritis Rheum. 2005;53(2):241-248
PubMed   |  Link to Article
Sokka T, Toloza S, Cutolo M,  et al; QUEST-RA Group.  Women, men, and rheumatoid arthritis.  Arthritis Res Ther. 2009;11(1):R7
PubMed
Brown AF, Ang A, Pebley AR. The relationship between neighborhood characteristics and self-rated health for adults with chronic conditions.  Am J Public Health. 2007;97(5):926-932
PubMed   |  Link to Article
Trupin L, Tonner MC, Yazdany J,  et al.  The role of neighborhood and individual socioeconomic status in outcomes of systemic lupus erythematosus.  J Rheumatol. 2008;35(9):1782-1788
PubMed
Zuckerman S, Waidmann T, Berenson R, Hadley J. Clarifying sources of geographic differences in Medicare spending.  N Engl J Med. 2010;363(1):54-62
PubMed   |  Link to Article
Song Y, Skinner J, Bynum J, Sutherland J, Wennberg JE, Fisher ES. Regional variations in diagnostic practices.  N Engl J Med. 2010;363(1):45-53
PubMed   |  Link to Article
Keenan PS, Cleary PD, O’Malley AJ, Landon BE, Zaborski L, Zaslavsky AM. Geographic area variations in the Medicare health plan era.  Med Care. 2010;48(3):260-266
PubMed   |  Link to Article
Schneider EC, Zaslavsky AM, Epstein AM. Quality of care in for-profit and not-for-profit health plans enrolling Medicare beneficiaries.  Am J Med. 2005;118(12):1392-1400
PubMed   |  Link to Article
Baker LC, Hopkins DS. The contribution of health plans and provider organizations to variations in measured plan quality.  Int J Qual Health Care. 2010;22(3):210-218
PubMed   |  Link to Article
Gabriel SE. The sensitivity and specificity of computerized databases for the diagnosis of rheumatoid arthritis.  Arthritis Rheum. 1994;37(6):821-823
PubMed   |  Link to Article
Polinski JM, Mohr PE, Johnson L. Impact of Medicare Part D on access to and cost sharing for specialty biologic medications for beneficiaries with rheumatoid arthritis.  Arthritis Rheum. 2009;61(6):745-754
PubMed   |  Link to Article
Gleason PP, Starner CI, Gunderson BW, Schafer JA, Sarran HS. Association of prescription abandonment with cost share for high-cost specialty pharmacy medications.  J Manag Care Pharm. 2009;15(8):648-658
PubMed

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